U.S. patent application number 15/490499 was filed with the patent office on 2017-10-26 for system fault diagnosis via efficient temporal and dynamic historical fingerprint retrieval.
The applicant listed for this patent is NEC Laboratories America, Inc.. Invention is credited to Haifeng Chen, Wei Cheng, Guofei Jiang, Kenji Yoshihira.
Application Number | 20170308427 15/490499 |
Document ID | / |
Family ID | 60089621 |
Filed Date | 2017-10-26 |
United States Patent
Application |
20170308427 |
Kind Code |
A1 |
Cheng; Wei ; et al. |
October 26, 2017 |
System Fault Diagnosis via Efficient Temporal and Dynamic
Historical Fingerprint Retrieval
Abstract
Methods are provided for both single modal and multimodal fault
diagnosis. In a method, a fault fingerprint is constructed based on
a fault event using an invariant model. A similarity matrix between
the fault fingerprint and one or more historical representative
fingerprints are derived using dynamic time warping and at least
one convolution. A feature vector in a feature subspace for the
fault fingerprint is generated. The feature vector includes at
least one status of at least one system component during the fault
event. A corrective action correlated to the fault fingerprint is
determined. The corrective action is initiated on a hardware device
to mitigate expected harm to at least one item selected from the
group consisting of the hardware device, another hardware device
related to the hardware device, and a person related to the
hardware device.
Inventors: |
Cheng; Wei; (Plainsboro,
NJ) ; Yoshihira; Kenji; (Princeton Junction, NJ)
; Chen; Haifeng; (West Windsor, NJ) ; Jiang;
Guofei; (Princeton, NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NEC Laboratories America, Inc. |
Princeton |
NJ |
US |
|
|
Family ID: |
60089621 |
Appl. No.: |
15/490499 |
Filed: |
April 18, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62327489 |
Apr 26, 2016 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 41/064 20130101;
G06F 11/0709 20130101; G06F 11/0793 20130101; G06F 11/079
20130101 |
International
Class: |
G06F 11/07 20060101
G06F011/07; G06F 11/07 20060101 G06F011/07 |
Claims
1. A computer-implemented method for single modal fault diagnosis,
the method comprising: constructing, by a processor using an
invariant model, a fault fingerprint based on a fault event;
deriving, by the processor using dynamic time warping and at least
one convolution, a similarity matrix between the fault fingerprint
and one or more historical representative fingerprints;
determining, by the processor, a corrective action correlated to
the fault fingerprint, from among a plurality of candidate
corrective actions associated with the one or more historical
representative fingerprints, based on a unity similarity obtained
by processing the similarity matrix; and initiating, by the
processor, the corrective action on a hardware device to mitigate
expected harm to at least one item selected from the group
consisting of the hardware device, another hardware device related
to the hardware device, and a person related to the hardware
device.
2. The computer-implemented method of claim 1, wherein the
invariant model learns continuous pair-wised correlations between
one or more system components during the fault event.
3. The computer-implemented method of claim 2, wherein the
invariant model computes a normalized residual between a
measurement for one of the pair-wised correlations and an estimate
for the one of the pair-wised correlations, wherein the normalized
residual exceeding a predetermined threshold signals one of the
pair-wised correlations are broken.
4. The computer-implemented method of claim 1, wherein the
invariant model includes an autoregressive model.
5. The computer-implemented method of claim 1, wherein each of the
one or more historical representative fingerprints represents a
category of historical fingerprints.
6. The computer-implemented method of claim 1, wherein the
convolutions use a sliding window for the fault fingerprint and the
one or more historical representative fingerprints.
7. The computer-implemented method of claim 1, wherein the unity
similarity includes a Jaccard similarity.
8. The computer-implemented method of claim 1, wherein the unity
similarity captures both a local temporal pattern and a multimodal
temporal pattern.
9. The computer-implemented method of claim 1, wherein the unity
similarity includes a maximum score from the convolutions.
10. A non-transitory article of manufacture tangibly embodying a
computer readable program which when executed causes a computer to
perform the steps of claim 1.
11. A computer-implemented method for multimodal fault diagnosis,
the method comprising: constructing, by a processor using an
invariant model, a fault fingerprint based on a fault event;
generating, by the processor, a feature vector in a feature
subspace for the fault fingerprint, wherein said feature vector
includes at least one status of at least one system component
during the fault event; determining, by the processor, a corrective
action correlated to the fault fingerprint, from among a plurality
of candidate corrective actions associated with the one or more
historical representative fingerprints, based on a Jaccard
similarity using the feature vector in the feature subspace; and
initiating, by the processor, the corrective action on a hardware
device to mitigate expected harm to at least one item selected from
the group consisting of the hardware device, another hardware
device related to the hardware device, and a person related to the
hardware device.
12. The computer-implemented method of claim 11, wherein the
invariant model learns continuous pair-wise correlations between
one or more system components during the fault event.
13. The computer-implemented method of claim 12, wherein the
invariant model computes a normalized residual between a
measurement for one of the pair-wise correlations and an estimate
for the one of the pair-wise correlations, wherein the normalized
residual exceeding a predetermined threshold signals the one of the
pair-wise correlations is broken.
14. The computer-implemented method of claim 11, wherein the
invariant model includes an autoregressive model.
15. The computer-implemented method of claim 11, further comprising
generating the feature subspace using one or more binary vectors
representing each of one or more pair-wise correlations in the
invariant model.
16. The computer-implemented method of claim 15, wherein the one or
more binary vectors has a value of 1 if the represented one or more
pair-wise correlations is broken and 0 otherwise.
17. The computer-implemented method of claim 15, wherein the
generating the feature subspace includes a chi-square feature
selection method using one or more categories of historical
fingerprints with one or more fault labels.
18. The computer-implemented method of claim 11, wherein the
generating the feature vector includes generating fault fingerprint
feature vectors from the fault fingerprint and one or more
historical representative fingerprint feature vectors from the one
or more historical representative fingerprints.
19. The computer-implemented method of claim 18, wherein the
generating the feature vector further includes mapping the fault
fingerprint feature vectors and the one or more historical
representative fingerprint feature vectors in the feature subspace
based on informative broken evidence.
20. A non-transitory article of manufacture tangibly embodying a
computer readable program which when executed causes a computer to
perform the steps of claim 11.
Description
RELATED APPLICATION INFORMATION
[0001] This application claims priority to U.S. Provisional Patent
Application Ser. No. 62/327,489 filed on Apr. 26, 2016,
incorporated herein by reference in its entirety.
BACKGROUND
Technical Field
[0002] The present invention generally relates to fault diagnosis
and more particularly to system fault diagnosis via efficient
temporal and dynamic historical fingerprint retrieval.
Description of the Related Art
[0003] A central task in running large scale distributed systems
and cyber-physical systems is to automatically monitor the system
status and diagnose system fault, so as to guarantee stable and
high-quality services or outputs. Significant research efforts have
been devoted to this topic. Traditional approaches rely on thorough
understandings of the system architecture to build system models
and the predefined rules for the diagnosis. With the increasing of
system complexity, it is hard, if not impossible, to obtain a
precise system architecture beforehand. Moreover, typically the
system statuses are quite dynamic with time evolving. Thus, it is
desirable to design an effective method that is able to
automatically diagnose system failure and give action suggestions.
Recently, the fault detection in distributed systems received
increasing attentions. One system proposed to model event
correlation and locate system faults using known dependency
relationships between faults and symptoms. In real applications,
however, it is usually hard to obtain such relationships precisely.
To alleviate this limitation, another system developed several
model-based approaches to detect the faults in complex distributed
systems. These approaches generally focus on locating the faulty
components, they are not capable of spotting or ranking the causal
anomalies, thus they are not able to give action suggestions.
SUMMARY
[0004] According to an aspect of the present principles, a
computer-implemented method is provided for single modal fault
diagnosis. The method includes constructing, by a processor using
an invariant model, a fault fingerprint based on a fault event. The
method also includes deriving, by the processor using dynamic time
warping and at least one convolution, a similarity matrix between
the fault fingerprint and one or more historical representative
fingerprints. The method additionally includes determining, by the
processor, a corrective action correlated to the fault fingerprint,
from among a plurality of candidate corrective actions associated
with the one or more historical representative fingerprints, based
on a unity similarity obtained by processing the similarity matrix.
The method also includes initiating, by the processor, the
corrective action on a hardware device to mitigate expected harm to
at least one item selected from the group consisting of the
hardware device, another hardware device related to the hardware
device, and a person related to the hardware device.
[0005] According to another aspect of the present principles, a
computer-implemented method is provided for multimodal fault
diagnosis. The method includes constructing, by a processor using
an invariant model, a fault fingerprint based on a fault event. The
method also includes generating, by the processor, a feature vector
in a feature subspace for the fault fingerprint, wherein said
feature vector includes at least one status of at least one system
component during the fault event. The method additionally includes
determining, by the processor, a corrective action correlated to
the fault fingerprint, from among a plurality of candidate
corrective actions associated with the one or more historical
representative fingerprints, based on a Jaccard similarity using
the feature vector in the feature subspace. The method also
includes initiating, by the processor, the corrective action on a
hardware device to mitigate expected harm to at least one item
selected from the group consisting of the hardware device, another
hardware device related to the hardware device, and a person
related to the hardware device.
[0006] These and other features and advantages will become apparent
from the following detailed description of illustrative embodiments
thereof, which is to be read in connection with the accompanying
drawings.
BRIEF DESCRIPTION OF DRAWINGS
[0007] The disclosure will provide details in the following
description of preferred embodiments with reference to the
following figures wherein:
[0008] FIG. 1 shows a block diagram of an exemplary processing
system to which the present invention may be applied, in accordance
with an embodiment of the present invention;
[0009] FIG. 2 shows a block diagram of an exemplary environment to
which the present invention can be applied, in accordance with an
embodiment of the present invention;
[0010] FIG. 3 is a block diagram illustrating a method for single
modal fault diagnosis, in accordance with an embodiment of the
present invention;
[0011] FIG. 4 shows a block/flow diagram illustrating a method for
both single modal and multimodal fault diagnosis, in accordance
with an embodiment of the present invention;
[0012] FIG. 5 is a block diagram illustrating a method for
multimodal fault diagnosis, in accordance with an embodiment of the
present invention; and
[0013] FIG. 6 shows a block diagram of an exemplary environment to
which the present invention can be applied, in accordance with an
embodiment of the present invention.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0014] Since many system faults also occur repeatedly, it is
possible to diagnose system failure by retrieval from a historical
symptom database. Specifically, an embodiment may extract the
system fingerprint based on an invariant model, which learns the
dependencies between system components. When a failure happens, a
sequence of broken invariants is recorded in a binary matrix to
represent the temporal and dynamic failure behavior. A fingerprint
database is built to store all such historical system fault
fingerprints as well as their failure reasons or possible action
annotations. Then the system fault diagnosis solves the problem of
symptom fingerprint retrieval, which highly depends on the
similarity measurement between a query fingerprint temporal matrix
and the historical fingerprint records. The case of single modal
symptom retrieval and the multimodal symptom retrieval are
decoupled. For a multimodal symptom, the fingerprint matrix is
compacted into a feature vector, then a chi-square feature
selection method is employed to select, out the most informative
broken dependencies, between system components for the fault
annotations. The new feature vector in the subspace is used to
calculate the similarity score. For a single modal symptom, an
effective metric, based on dynamic time warping and sequence
convolution, is defined to measure the similarity between query
fingerprint and historical representative fingerprint records. The
metric is able to extract single modal temporal features.
[0015] FIG. 1 shows a block diagram of an exemplary processing
system 100 to which the invention principles may be applied, in
accordance with an embodiment of the present invention. The
processing system 100 includes at least one processor (CPU) 104
operatively coupled to other components via a system bus 102. A
cache 106, a Read Only Memory (ROM) 108, a Random Access Memory
(RAM) 110, an input/output (I/O) adapter 120, a sound adapter 130,
a network adapter 140, a user interface adapter 150, and a display
adapter 160, are operatively coupled to the system bus 102.
[0016] A first storage device 122 and a second storage device 124
are operatively coupled to system bus 102 by the I/O adapter 120.
The storage devices 122 and 124 can be any of a disk storage device
(e.g., a magnetic or optical disk storage device), a solid state
magnetic device, and so forth. The storage devices 122 and 124 can
be the same type of storage device or different types of storage
devices.
[0017] A speaker 132 is operatively coupled to system bus 102 by
the sound adapter 130. The speaker 132 can be used to provide an
audible alarm or some other indication relating to resilient
battery charging in accordance with the present invention. A
transceiver 142 is operatively coupled to system bus 102 by network
adapter 140. A display device 162 is operatively coupled to system
bus 102 by display adapter 160.
[0018] A first user input device 152, a second user input device
154, and a third user input device 156 are operatively coupled to
system bus 102 by user interface adapter 150. The user input
devices 152, 154, and 156 can be any of a keyboard, a mouse, a
keypad, an image capture device, a motion sensing device, a
microphone, a device incorporating the functionality of at least
two of the preceding devices, and so forth. Of course, other types
of input devices can also be used, while maintaining the spirit of
the present invention. The user input devices 152, 154, and 156 can
be the same type of user input device or different types of user
input devices. The user input devices 152, 154, and 156 are used to
input and output information to and from system 100.
[0019] Of course, the processing system 100 may also include other
elements (not shown), as readily contemplated by one of skill in
the art, as well as omit certain elements. For example, various
other input devices and/or output devices can be included in
processing system 100, depending upon the particular implementation
of the same, as readily understood by one of ordinary skill in the
art. For example, various types of wireless and/or wired input
and/or output devices can be used. Moreover, additional processors,
controllers, memories, and so forth, in various configurations can
also be utilized as readily appreciated by one of ordinary skill in
the art. These and other variations of the processing system 100
are readily contemplated by one of ordinary skill in the an given
the teachings of the present invention provided herein.
[0020] Moreover, it is to be appreciated that environment 200 and
environment 600 described below with respect to FIG. 2 and FIG. 6
are environments for implementing respective embodiments of the
present invention. Part or all of processing system 100 may be
implemented in one or more of the elements of environment 200
and/or one or more of the elements of environment 600.
[0021] Further, it is to be appreciated that processing system 100
may perform at least part of the method described herein including,
for example, at least part of method 300 of FIG. 3 and/or at least
part of method 400 of FIG. 4 and/or at least part of method 500 of
FIG. 5. Similarly, part or all of system 210 and/or system 610 may
be used to perform at least part of method 300 of FIG. 3 and/or at
least part of method 400 of FIG. 4 and/or at least part of method
500 of FIG. 5.
[0022] FIG. 2 shows an exemplary environment 200 to which the
present invention can be applied, in accordance with an embodiment
of the present invention. The environment 200 is representative of
a computer network to which the present invention can be applied.
The elements shown relative to FIG. 2 are set forth for the sake of
illustration. However, it is to be appreciated that the present
invention can be applied to other network configurations as readily
contemplated by one of ordinary skill in the art given the
teachings of the present invention provided herein, while
maintaining the spirit of the present invention.
[0023] The environment 200 at least includes a set of computer
processing systems 210. The computer processing systems 210 can be
any type of computer processing system including, but not limited
to, servers, desktops, laptops, tablets, smart phones, media
playback devices, and so forth. For the sake of illustration, the
computer processing systems 210 include server 210A, server 210B,
and server 210C.
[0024] In an embodiment, the present invention performs system
fault diagnosis via efficient temporal and dynamic historical
fingerprint retrieval on the computer processing systems 210. Thus,
any of the computer processing systems 210 can perform system fault
diagnosis via efficient temporal and dynamic historical fingerprint
retrieval that produce a fault event, or accessed by, any of the
computer processing systems 210. Moreover, the output (including
corrective actions) of the present invention can be used to control
other systems and/or devices and/or operations and/or so forth, as
readily appreciated by one of ordinary skill in the art given the
teachings of the present invention provided herein, while
maintaining the spirit of the present invention.
[0025] In the embodiment shown in FIG. 2, the elements thereof are
interconnected by a network(s) 201. However, in other embodiments,
other types of connections can also be used. Additionally, one or
more elements in FIG. 2 may be implemented by a variety of devices,
which include but are not limited to, Digital Signal Processing
(DSP) circuits, programmable processors, Application Specific
Integrated Circuits (ASICs), Field Programmable Gate Arrays
(FPGAs), Complex Programmable Logic Devices (CPLDs), and so forth.
These and other variations of the elements of environment 200 are
readily determined by one of ordinary skill in the art, given the
teachings of the present invention provided herein, while
maintaining the spirit of the present invention.
[0026] Referring to FIG. 3, a block diagram illustrating a single
modal fault diagnosis method 300, in accordance with an embodiment
of the present invention. In block 310, construct, using an
invariant model, a fault fingerprint based on a fault event. In
block 320, derive, using dynamic time warping and at least one
convolution, a similarity matrix between the fault fingerprint and
one or more historical representative fingerprints. In block 330,
determine a corrective action correlated to the fault fingerprint,
from among a plurality of candidate corrective actions associated
with the one or more historical representative fingerprints, based
on a unity similarity obtained by processing the similarity matrix.
In block 340, initiate the corrective action on a hardware device
to mitigate expected harm to at least one item selected from the
group consisting of the hardware device, another hardware device
related to the hardware device, and a person related to the
hardware device.
[0027] This method achieves accurate diagnosis for complex and
dynamic temporal fingerprints with the help of historical failure
experiences. The advantages of this method are two-fold. (A) The
case of single modal fingerprint is decoupled from the multimodal
fingerprint cases, with different similarity measures adopted for
each. As a result, the method can extract accurate representative
fingerprints for each historical system faults and better calculate
the incoming new query fingerprint of failure event. (B) Effective
approaches are developed to calculate the pair-wise fingerprints
similarity. This approach for single modal fingerprints similarity
measurement will capture both spatial and multiple temporal
evidences encoded in the fault fingerprints.
[0028] The fault fingerprint retrieval process adopts an effective
feature selection procedure to extract the most informative broken
correlations between system components. This gives two critical
advantages: (A) since only a small portion of the broken
correlations are selected based on their importance, the similarity
measurement between query fingerprints and the historical
fingerprints is more robust to noise, and thus more accurate fault
diagnosis can be obtained; (B) due to the fact that the similarity
calculation is on a lower space, the time complexity of it can be
significantly reduced.
[0029] An efficient indexing strategy for historical fingerprints
is deployed. The index of each historical fingerprint is referred
to as the representative fingerprint. For each query fingerprint,
the method only needs to calculate the similarity to each achieved
representative fingerprint. As a result, the searching space is
significantly reduced. Actually, a constant number of fingerprint
(the same as the number of types of fault) similarity measurements
are needed, even when the fault fingerprint database increases as
more new fingerprints are archived with system running. Thus,
better online time performance can be achieved.
FIG. 4 shows a block/flow diagram illustrating both a single modal
and a multimodal fault diagnosis method 400, in accordance with an
embodiment of the present invention. The both the single modal and
the multimodal fault diagnosis method 400 may have a fault event
detected in step 410. Step 410 may feed in to step 420 that
constructs the fault fingerprint matrix. Step 420 may train an
invariant model using time series data during system normal periods
and use online tests when system invariants are broken during
system running periods. The invariant model learns the continuous
pair-wised correlations between different system components. The
invariant model may include an autoregressive model. Existing
systems may be used to track vanishing correlations. At each time
point, the (normalized) residual between the measurement and the
measurement's estimate are computed. If the residual exceeds a
predefined threshold, then the invariant is declared as "broken" as
the correlation between the two time series vanishes. Step 420 may
construct a temporal and spatial fingerprint matrix, the temporal
and spatial fingerprint matrix may include which pairs of
components are broken and at which time points the components are
broken.
[0030] After step 420, determine 422 based on the fault event
whether to perform a single modal process or a multimodal process
on the fault fingerprint. If it is determined to perform a single
modal process, then proceed to step 450. Otherwise, proceed to step
430. For the single modal process, step 420 will feed into step 450
for similarity retrieval based on a dynamic time warping and
convolutions. Step 450 may use one representative fingerprint as
the index for each category of historical fingerprints. Step 450
may calculate the similarity between query and historical
fingerprints using a combination of dynamic time warping and
convolution. The query and historical fingerprints using a
combination of dynamic time warping and convolution may use a
sliding window on both query and historical representative
fingerprints. The convolution similarity between each sliding
window for query and historical record is calculated. The maximum
of the convolution score is used as the similarity. f(t) is denoted
as the invariant status vector at time t, g(t) is another invariant
sequence. The convolution score is c(t)=f(t)g(t), and the
similarity s.sub.fg=max.sub.t{c(t)}. The unity similarity for each
vector pair may be based on a Jaccard similarity:
i [ f ( t ) .LAMBDA. g ( t ) ] i [ f ( t ) V g ( t ) ] i .
##EQU00001##
The similarity calculation between two window blocks may be based
in convolution. The overall similarity may be given by dynamic time
warping. The dynamic time warping is able to capture both local
temporal patterns and the multimodal temporal patterns.
[0031] For the multimodal process, step 420 will feed into step 430
to generate a feature vector based on a feature subspace. Step 430
may include step 430A that selects features to generate a feature
subspace. Step 430A may use a binary vector standing for the union
of evidences over the time to denote the value of each pair-wise
correlation in the invariant model, with 1 denoting broken
evidences during the time and 0 denoting non-broken evidences
during the time. The chi-square feature selection method may be
used on the historical fingerprints together with the fault/action
labels to learn the most informative broken evidence. The selected
most informative broken evidences may be updated by doing the
feature selection in batch mode.
[0032] Step 430 may transfer both the historical representative
fingerprints and the query fingerprint into binary feature vectors.
Then, both the historical representative fingerprint feature
vectors and the query fingerprint feature vectors are mapped into a
feature vector subspace based on the selected most informative
broken evidences. Step 430 may feed into step 440 that retrieves a
feature vector based similarity using a Jaccard similarity.
[0033] Step 440 or step 450 may be used to feed into step 460 that
suggests the historical action, depending on if the single modal or
the multimodal was produced in step 420.
[0034] Step 460 may be used to improve the both the single modal
and the multimodal fault diagnosis method 400. Step 460 may be used
in step 470 to archive new fingerprints and update representative
fingerprints. Step 470 may search a fingerprint database for the
query fingerprint. If the search finds a match in the fingerprint
database, then the representative fingerprint for the category that
the query fingerprint belongs to may be updated. If the search does
not find the query fingerprint in the fingerprint database, then
the query fingerprint may be recorded as a new category or the
query fingerprint may be recorded into a given category. The
recorded instance may be the original matrix for the multimodal
case or a feature vector for the single modal case.
[0035] Step 460 may initiate an action (e.g., a control action) on
a controlled system, machine, and/or device responsive to the fault
event detected and the action annotations attached to the
representative fingerprint for the fault event detected in the
fingerprint database. Such action can include, but is not limited
to, one or more of: powering down the controlled system, machine,
and/or device or a portion thereof; powering down, e.g., a system,
machine, and/or a device that is affected by the fault event
detected in another device, stopping a centrifuge when an imbalance
is detected, opening a valve to relieve excessive pressure
(depending upon the fault event detected), locking an automatic
fire door, and so forth. As is evident to one of ordinary skill in
the art, the action taken is dependent upon the fault event
detected and the controlled system, machine, and/or device to which
the action is applied.
[0036] Referring to FIG. 5, a block diagram illustrating a
multimodal fault diagnosis method 500, in accordance with an
embodiment of the present invention. In block 510, construct, using
an invariant model, a fault fingerprint based on a fault event. In
block 520, generate a feature vector in a feature subspace for the
fault fingerprint, wherein said feature vector includes at least
one status of at least one system component during the fault event.
In block 530, determine a corrective action correlated to the fault
fingerprint, from among a plurality of candidate corrective actions
associated with the one or more historical representative
fingerprints, based on a Jaccard similarity using the feature
vector in the feature subspace. In block 540, initiate the
corrective action on a hardware device to mitigate expected harm to
at least one item selected from the group consisting of the
hardware device, another hardware device related to the hardware
device, and a person related to the hardware device.
[0037] FIG. 6 shows a block diagram of an exemplary environment 600
to which the present invention can be applied, in accordance with
an embodiment of the present invention. The environment 600 is
representative of a computer network to which the present invention
can be applied. The elements shown relative to FIG. 6 are set forth
for the sake of illustration. However, it is to be appreciated that
the present invention can be applied to other network
configurations and other operational environments as readily
contemplated by one of ordinary skill in the art given the
teachings of the present invention provided herein, while
maintaining the spirit of the present invention.
[0038] The environment 600 at least includes at least one safety
system or device 602, at least one fault detection system 605, at
least one computer processing system 610, at least one controlled
system(s), machine(s), and/or device(s) (individually and
collectively denoted by the reference numeral 620 and hereinafter
referred to as "controlled system, machine, and/or device"). For
the sake of simplicity and illustration, the preceding elements are
shown in singular form, but can be readily extended to more than
one of any of the preceding elements as readily appreciated by one
of ordinary skill in the art given the teachings of the present
invention provided herein, while maintaining the spirit of the
present invention. The computer processing system 610 can be any
type of computer processing system including, but not limited to,
servers, desktops, laptops, tablets, smart phones, media playback
devices, and so forth, depending upon the particular
implementation. For the sake of illustration, the computer
processing system 610 is a server.
[0039] The at least one fault detection system 605 is configured to
detect one or more fault events. The computer processing system 610
is configured to perform fault diagnosis via efficient temporal and
dynamic historical fingerprint retrieval. Moreover, the computer
processing system 610 is configured to initiate an action (e.g., a
control action) on the controlled system, machine, and/or device
620 responsive to the detected fault event. Such action can
include, but is not limited to, one or more of: powering down the
controlled system, machine, and/or device 620 or a portion thereof;
powering down, e.g., a system, machine, and/or a device that is
affected by an anomaly in another device, stopping a centrifuge
being operated by a user 620A before an imbalance in the centrifuge
causes a critical failure and harm to the user 620A, opening a
valve to relieve excessive pressure (depending upon the anomaly),
locking an automatic fire door, and so forth. As is evident to one
of ordinary skill in the art, the action taken is dependent upon
the type of anomaly and the controlled system, machine, and/or
device 620 to which the action is applied.
[0040] The safety system or device 602 can implement the
aforementioned or other action. The safety system or device 602 can
be a shut off switch, a fire suppression system, an overpressure
valve, and so forth. As is readily appreciated by one of ordinary
skill in the art, the particular safety system or device 602 used
depends upon the particular implementation to which the present
invention is applied. Hence, the safety system 602 can be located
within or proximate to or remote from the controlled system,
machine, and/or device 620, depending upon the particular
implementation.
[0041] In the embodiment shown in FIG. 6, the elements thereof are
interconnected by a network(s) 601. However, in other embodiments,
other types of connections (e.g., wired, etc.) can also be used.
Additionally, one or more elements in FIG. 6 may be implemented by
a variety of devices, which include but are not limited to, Digital
Signal Processing (DSP) circuits, programmable processors,
Application Specific Integrated Circuits (ASICs), Field
Programmable Gate Arrays (FPGAs), Complex Programmable Logic
Devices (CPLDs), and so forth. These and other variations of the
elements of environment 200 are readily determined by one of
ordinary skill in the art, given the teachings of the present
invention provided herein, while maintaining the spirit of the
present invention.
[0042] Embodiments described herein may be entirely hardware,
entirely software or including both hardware and software elements.
In a preferred embodiment, the present invention is implemented in
software, which includes but is not limited to firmware, resident
software, microcode, etc.
[0043] Embodiments may include a computer program product
accessible from a computer-usable or computer-readable medium
providing program code for use by or in connection with a computer
or any instruction execution system. A computer-usable or computer
readable medium may include any apparatus that stores,
communicates, propagates, or transports the program for use by or
in connection with the instruction execution system, apparatus, or
device. The medium can be magnetic, optical, electronic,
electromagnetic, infrared, or semiconductor system (or apparatus or
device) or a propagation medium. The medium may include a
computer-readable storage medium such as a semiconductor or solid
state memory, magnetic tape, a removable computer diskette, a
random access memory (RAM), a read-only memory (ROM), a rigid
magnetic disk and an optical disk, etc.
[0044] Each computer program may be tangibly stored in a
machine-readable storage media or device (e.g., program memory or
magnetic disk) readable by a general or special purpose
programmable computer, for configuring and controlling operation of
a computer when the storage media or device is read by the computer
to perform the procedures described herein. The inventive system
may also be considered to be embodied in a computer-readable
storage medium, configured with a computer program, where the
storage medium so configured causes a computer to operate in a
specific and predefined manner to perform the functions described
herein.
[0045] A data processing system suitable for storing and/or
executing program code may include at least one processor coupled
directly or indirectly to memory elements through a system bus. The
memory elements can include local memory employed during actual
execution of the program code, bulk storage, and cache memories
which provide temporary storage of at least some program code to
reduce the number of times code is retrieved from bulk storage
during execution. Input/output or I/O devices (including but not
limited to keyboards, displays, pointing devices, etc.) may be
coupled to the system either directly or through intervening I/O
controllers.
[0046] Network adapters may also be coupled to the system to enable
the data processing system to become coupled to other data
processing systems or remote printers or storage devices through
intervening private or public networks. Modems, cable modem and
Ethernet cards are just a few of the currently available types of
network adapters.
[0047] Reference in the specification to "one embodiment" or "an
embodiment" of the present invention, as well as other variations
thereof, means that a particular feature, structure,
characteristic, and so forth described in connection with the
embodiment is included in at least one embodiment of the present
invention. Thus, the appearances of the phrase "in one embodiment"
or "in an embodiment", as well any other variations, appearing in
various places throughout the specification are not necessarily all
referring to the same embodiment.
[0048] It is to be appreciated that the use of any of the following
"/", "and/or", and "at least one of", for example, in the cases of
"AB", "A and/or B" and "at least one of A and B", is intended to
encompass the selection of the first listed option (A) only, or the
selection of the second listed option (B) only, or the selection of
both options (A and B). As a further example, in the cases of "A,
B, and/or C" and "at least one of A, B, and C", such phrasing is
intended to encompass the selection of the first listed option (A)
only, or the selection of the second listed option (B) only, or the
selection of the third listed option (C) only, or the selection of
the first and the second listed options (A and B) only, or the
selection of the first and third listed options (A and C) only, or
the selection of the second and third listed options (B and C)
only, or the selection of all three options (A and B and C). This
may be extended, as readily apparent by one of ordinary skill in
this and related arts, for as many items listed.
[0049] The foregoing is to be understood as being in every respect
illustrative and exemplary, but not restrictive, and the scope of
the invention disclosed herein is not to be determined from the
Detailed Description, but rather from the claims as interpreted
according to the full breadth permitted by the patent laws. It is
to be understood that the embodiments shown and described herein
are only illustrative of the principles of the present invention
and that those skilled in the art may implement various
modifications without departing from the scope and spirit of the
invention. Those skilled in the art could implement various other
feature combinations without departing from the scope and spirit of
the invention. Having thus described aspects of the invention, with
the details and particularity required by the patent laws, what is
claimed and desired protected by Letters Patent is set forth in the
appended claims.
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